National emission inventories of ozone-depleting substances (ODS) play a key role in the control mechanisms of the Montreal Protocol's emission reduction plans. New quasi-continuous ground-based atmospheric measurements allow us to estimate China's current emissions of the most effective ODS. This serves as an independent validation of China's ODS consumption data reported to the United Nations Environment Programme (UNEP). Emissions of most first-generation ODS have declined in recent years, suggesting compliance with the regulations of China's advanced phase-out program. In contrast the emissions of some second-generation ODS have increased. Because China is currently one of the largest consumers of first generation ODS, the country's upcoming complete phase-out will be crucial for the rate of decline of atmospheric ODS hence the eventual recovery of the stratospheric ozone. Citation: Vollmer, M. K., et al. (2009), Emissions of ozone-depleting halocarbons from China, Geophys. Res. Lett., 36, L15823, doi:10.1029/2009GL038659

Background Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013–2015 in North Plain China. Results Results showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CI green ), red edge normalized ratio (NR red edge ), and red-edge chlorophyll index (CI red edge ) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT. Conclusions Compared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.